Overview

Dataset statistics

Number of variables29
Number of observations83771
Missing cells156255
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 MiB
Average record size in memory167.0 B

Variable types

Categorical21
Numeric8

Alerts

diag_1 has a high cardinality: 704 distinct valuesHigh cardinality
diag_2 has a high cardinality: 726 distinct valuesHigh cardinality
diag_3 has a high cardinality: 764 distinct valuesHigh cardinality
admission_type_id is highly overall correlated with Admission_SourceHigh correlation
insulin is highly overall correlated with change and 1 other fieldsHigh correlation
change is highly overall correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly overall correlated with insulin and 1 other fieldsHigh correlation
Admission_Source is highly overall correlated with admission_type_idHigh correlation
race is highly imbalanced (56.3%)Imbalance
max_glu_serum is highly imbalanced (52.8%)Imbalance
metformin is highly imbalanced (58.9%)Imbalance
Meglitinides is highly imbalanced (91.0%)Imbalance
Thiazolidinediones is highly imbalanced (69.2%)Imbalance
Alpha-Gluc_Inhibitors is highly imbalanced (98.3%)Imbalance
Combination Therapies is highly imbalanced (96.4%)Imbalance
race has 1994 (2.4%) missing valuesMissing
diag_3 has 1164 (1.4%) missing valuesMissing
max_glu_serum has 83461 (99.6%) missing valuesMissing
A1Cresult has 69314 (82.7%) missing valuesMissing
number_emergency is highly skewed (γ1 = 23.58268378)Skewed
num_procedures has 38590 (46.1%) zerosZeros
number_outpatient has 70604 (84.3%) zerosZeros
number_emergency has 74290 (88.7%) zerosZeros
number_inpatient has 55674 (66.5%) zerosZeros

Reproduction

Analysis started2023-10-20 00:20:29.051672
Analysis finished2023-10-20 00:20:49.918198
Duration20.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

race
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1994
Missing (%)2.4%
Memory size736.5 KiB
Caucasian
61994 
AfricanAmerican
16515 
Hispanic
 
1486
Other
 
1239
Asian
 
543

Length

Max length15
Median length9
Mean length10.106375
Min length5

Characters and Unicode

Total characters826469
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowAfricanAmerican
3rd rowCaucasian
4th rowCaucasian
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian 61994
74.0%
AfricanAmerican 16515
 
19.7%
Hispanic 1486
 
1.8%
Other 1239
 
1.5%
Asian 543
 
0.6%
(Missing) 1994
 
2.4%

Length

2023-10-19T20:20:50.057518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:50.268132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
caucasian 61994
75.8%
africanamerican 16515
 
20.2%
hispanic 1486
 
1.8%
other 1239
 
1.5%
asian 543
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 221041
26.7%
i 98539
11.9%
n 97053
11.7%
c 96510
11.7%
s 64023
 
7.7%
C 61994
 
7.5%
u 61994
 
7.5%
r 34269
 
4.1%
A 33573
 
4.1%
e 17754
 
2.1%
Other values (7) 39719
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 728177
88.1%
Uppercase Letter 98292
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 221041
30.4%
i 98539
13.5%
n 97053
13.3%
c 96510
13.3%
s 64023
 
8.8%
u 61994
 
8.5%
r 34269
 
4.7%
e 17754
 
2.4%
f 16515
 
2.3%
m 16515
 
2.3%
Other values (3) 3964
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 61994
63.1%
A 33573
34.2%
H 1486
 
1.5%
O 1239
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 826469
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 221041
26.7%
i 98539
11.9%
n 97053
11.7%
c 96510
11.7%
s 64023
 
7.7%
C 61994
 
7.5%
u 61994
 
7.5%
r 34269
 
4.1%
A 33573
 
4.1%
e 17754
 
2.1%
Other values (7) 39719
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 826469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 221041
26.7%
i 98539
11.9%
n 97053
11.7%
c 96510
11.7%
s 64023
 
7.7%
C 61994
 
7.5%
u 61994
 
7.5%
r 34269
 
4.1%
A 33573
 
4.1%
e 17754
 
2.1%
Other values (7) 39719
 
4.8%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size736.4 KiB
Female
45295 
Male
38476 

Length

Max length6
Median length6
Mean length5.0814005
Min length4

Characters and Unicode

Total characters425674
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 45295
54.1%
Male 38476
45.9%

Length

2023-10-19T20:20:50.434588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:50.621374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 45295
54.1%
male 38476
45.9%

Most occurring characters

ValueCountFrequency (%)
e 129066
30.3%
a 83771
19.7%
l 83771
19.7%
F 45295
 
10.6%
m 45295
 
10.6%
M 38476
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 341903
80.3%
Uppercase Letter 83771
 
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 129066
37.7%
a 83771
24.5%
l 83771
24.5%
m 45295
 
13.2%
Uppercase Letter
ValueCountFrequency (%)
F 45295
54.1%
M 38476
45.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 425674
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 129066
30.3%
a 83771
19.7%
l 83771
19.7%
F 45295
 
10.6%
m 45295
 
10.6%
M 38476
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 129066
30.3%
a 83771
19.7%
l 83771
19.7%
F 45295
 
10.6%
m 45295
 
10.6%
M 38476
 
9.0%

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size736.6 KiB
[70-80)
21024 
[60-70)
18813 
[50-60)
14429 
[80-90)
13985 
[40-50)
7961 
Other values (5)
7559 

Length

Max length8
Median length7
Mean length7.0247699
Min length6

Characters and Unicode

Total characters588472
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[10-20)
2nd row[20-30)
3rd row[30-40)
4th row[40-50)
5th row[50-60)

Common Values

ValueCountFrequency (%)
[70-80) 21024
25.1%
[60-70) 18813
22.5%
[50-60) 14429
17.2%
[80-90) 13985
16.7%
[40-50) 7961
 
9.5%
[30-40) 3155
 
3.8%
[90-100) 2219
 
2.6%
[20-30) 1419
 
1.7%
[10-20) 622
 
0.7%
[0-10) 144
 
0.2%

Length

2023-10-19T20:20:50.768206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:50.984879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
70-80 21024
25.1%
60-70 18813
22.5%
50-60 14429
17.2%
80-90 13985
16.7%
40-50 7961
 
9.5%
30-40 3155
 
3.8%
90-100 2219
 
2.6%
20-30 1419
 
1.7%
10-20 622
 
0.7%
0-10 144
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 169761
28.8%
[ 83771
14.2%
- 83771
14.2%
) 83771
14.2%
7 39837
 
6.8%
8 35009
 
5.9%
6 33242
 
5.6%
5 22390
 
3.8%
9 16204
 
2.8%
4 11116
 
1.9%
Other values (3) 9600
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 337159
57.3%
Open Punctuation 83771
 
14.2%
Dash Punctuation 83771
 
14.2%
Close Punctuation 83771
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169761
50.4%
7 39837
 
11.8%
8 35009
 
10.4%
6 33242
 
9.9%
5 22390
 
6.6%
9 16204
 
4.8%
4 11116
 
3.3%
3 4574
 
1.4%
1 2985
 
0.9%
2 2041
 
0.6%
Open Punctuation
ValueCountFrequency (%)
[ 83771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83771
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 588472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169761
28.8%
[ 83771
14.2%
- 83771
14.2%
) 83771
14.2%
7 39837
 
6.8%
8 35009
 
5.9%
6 33242
 
5.6%
5 22390
 
3.8%
9 16204
 
2.8%
4 11116
 
1.9%
Other values (3) 9600
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169761
28.8%
[ 83771
14.2%
- 83771
14.2%
) 83771
14.2%
7 39837
 
6.8%
8 35009
 
5.9%
6 33242
 
5.6%
5 22390
 
3.8%
9 16204
 
2.8%
4 11116
 
1.9%
Other values (3) 9600
 
1.6%

admission_type_id
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size736.6 KiB
1
49155 
3
17574 
2
17024 
7
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83771
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 49155
58.7%
3 17574
 
21.0%
2 17024
 
20.3%
7 18
 
< 0.1%

Length

2023-10-19T20:20:51.168120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:51.318084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 49155
58.7%
3 17574
 
21.0%
2 17024
 
20.3%
7 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 49155
58.7%
3 17574
 
21.0%
2 17024
 
20.3%
7 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83771
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49155
58.7%
3 17574
 
21.0%
2 17024
 
20.3%
7 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 83771
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49155
58.7%
3 17574
 
21.0%
2 17024
 
20.3%
7 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49155
58.7%
3 17574
 
21.0%
2 17024
 
20.3%
7 18
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3743897
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:51.467899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9587451
Coefficient of variation (CV)0.67637894
Kurtosis0.92065395
Mean4.3743897
Median Absolute Deviation (MAD)2
Skewness1.14973
Sum366447
Variance8.7541723
MonotonicityNot monotonic
2023-10-19T20:20:51.642430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 14714
17.6%
2 14453
17.3%
4 11581
13.8%
1 11452
13.7%
5 8261
9.9%
6 6182
7.4%
7 4850
 
5.8%
8 3560
 
4.2%
9 2405
 
2.9%
10 1895
 
2.3%
Other values (4) 4418
 
5.3%
ValueCountFrequency (%)
1 11452
13.7%
2 14453
17.3%
3 14714
17.6%
4 11581
13.8%
5 8261
9.9%
6 6182
7.4%
7 4850
 
5.8%
8 3560
 
4.2%
9 2405
 
2.9%
10 1895
 
2.3%
ValueCountFrequency (%)
14 851
 
1.0%
13 954
 
1.1%
12 1142
 
1.4%
11 1471
 
1.8%
10 1895
 
2.3%
9 2405
 
2.9%
8 3560
4.2%
7 4850
5.8%
6 6182
7.4%
5 8261
9.9%

num_lab_procedures
Real number (ℝ)

Distinct114
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.449535
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:51.787804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q133
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.308033
Coefficient of variation (CV)0.44437836
Kurtosis-0.11281947
Mean43.449535
Median Absolute Deviation (MAD)12
Skewness-0.33162753
Sum3639811
Variance372.80014
MonotonicityNot monotonic
2023-10-19T20:20:52.007332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2803
 
3.3%
43 2437
 
2.9%
44 2126
 
2.5%
45 2040
 
2.4%
38 1958
 
2.3%
40 1945
 
2.3%
46 1860
 
2.2%
39 1846
 
2.2%
47 1819
 
2.2%
37 1816
 
2.2%
Other values (104) 63121
75.3%
ValueCountFrequency (%)
1 2803
3.3%
2 1014
 
1.2%
3 615
 
0.7%
4 347
 
0.4%
5 227
 
0.3%
6 228
 
0.3%
7 266
 
0.3%
8 302
 
0.4%
9 699
 
0.8%
10 684
 
0.8%
ValueCountFrequency (%)
132 1
 
< 0.1%
126 1
 
< 0.1%
121 1
 
< 0.1%
118 1
 
< 0.1%
113 3
< 0.1%
111 1
 
< 0.1%
109 2
< 0.1%
108 4
< 0.1%
106 3
< 0.1%
105 2
< 0.1%

num_procedures
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3377302
Minimum0
Maximum6
Zeros38590
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:52.184540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7098292
Coefficient of variation (CV)1.278157
Kurtosis0.86386071
Mean1.3377302
Median Absolute Deviation (MAD)1
Skewness1.3214307
Sum112063
Variance2.923516
MonotonicityNot monotonic
2023-10-19T20:20:52.334812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 38590
46.1%
1 16970
20.3%
2 10424
 
12.4%
3 7695
 
9.2%
6 4146
 
4.9%
4 3446
 
4.1%
5 2500
 
3.0%
ValueCountFrequency (%)
0 38590
46.1%
1 16970
20.3%
2 10424
 
12.4%
3 7695
 
9.2%
4 3446
 
4.1%
5 2500
 
3.0%
6 4146
 
4.9%
ValueCountFrequency (%)
6 4146
 
4.9%
5 2500
 
3.0%
4 3446
 
4.1%
3 7695
 
9.2%
2 10424
 
12.4%
1 16970
20.3%
0 38590
46.1%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.975194
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:52.517931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.0993034
Coefficient of variation (CV)0.50699248
Kurtosis3.6348773
Mean15.975194
Median Absolute Deviation (MAD)5
Skewness1.3412093
Sum1338258
Variance65.598716
MonotonicityNot monotonic
2023-10-19T20:20:52.675676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 5023
 
6.0%
12 4966
 
5.9%
15 4807
 
5.7%
14 4749
 
5.7%
11 4744
 
5.7%
16 4466
 
5.3%
10 4444
 
5.3%
9 4052
 
4.8%
17 4042
 
4.8%
18 3716
 
4.4%
Other values (65) 38762
46.3%
ValueCountFrequency (%)
1 224
 
0.3%
2 403
 
0.5%
3 733
 
0.9%
4 1186
 
1.4%
5 1647
 
2.0%
6 2246
2.7%
7 2906
3.5%
8 3553
4.2%
9 4052
4.8%
10 4444
5.3%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%
70 2
 
< 0.1%
69 5
< 0.1%
68 6
< 0.1%
67 7
< 0.1%
66 5
< 0.1%

number_outpatient
Real number (ℝ)

ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35389335
Minimum0
Maximum42
Zeros70604
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:52.868189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2494166
Coefficient of variation (CV)3.5304891
Kurtosis148.75757
Mean0.35389335
Median Absolute Deviation (MAD)0
Skewness8.8942539
Sum29646
Variance1.5610419
MonotonicityNot monotonic
2023-10-19T20:20:53.065611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 70604
84.3%
1 6791
 
8.1%
2 2782
 
3.3%
3 1580
 
1.9%
4 854
 
1.0%
5 396
 
0.5%
6 237
 
0.3%
7 135
 
0.2%
8 85
 
0.1%
9 66
 
0.1%
Other values (28) 241
 
0.3%
ValueCountFrequency (%)
0 70604
84.3%
1 6791
 
8.1%
2 2782
 
3.3%
3 1580
 
1.9%
4 854
 
1.0%
5 396
 
0.5%
6 237
 
0.3%
7 135
 
0.2%
8 85
 
0.1%
9 66
 
0.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
35 1
< 0.1%
34 1
< 0.1%
33 2
< 0.1%
29 1
< 0.1%
28 1
< 0.1%

number_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20259995
Minimum0
Maximum76
Zeros74290
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:53.216085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96853613
Coefficient of variation (CV)4.780535
Kurtosis1216
Mean0.20259995
Median Absolute Deviation (MAD)0
Skewness23.582684
Sum16972
Variance0.93806224
MonotonicityNot monotonic
2023-10-19T20:20:53.392693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 74290
88.7%
1 6410
 
7.7%
2 1656
 
2.0%
3 600
 
0.7%
4 323
 
0.4%
5 162
 
0.2%
6 80
 
0.1%
7 64
 
0.1%
8 42
 
0.1%
10 33
 
< 0.1%
Other values (23) 111
 
0.1%
ValueCountFrequency (%)
0 74290
88.7%
1 6410
 
7.7%
2 1656
 
2.0%
3 600
 
0.7%
4 323
 
0.4%
5 162
 
0.2%
6 80
 
0.1%
7 64
 
0.1%
8 42
 
0.1%
9 30
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
54 1
< 0.1%
46 1
< 0.1%
42 1
< 0.1%
37 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
25 1
< 0.1%

number_inpatient
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63751179
Minimum0
Maximum21
Zeros55674
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:53.566338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2692575
Coefficient of variation (CV)1.9909553
Kurtosis19.849244
Mean0.63751179
Median Absolute Deviation (MAD)0
Skewness3.5789077
Sum53405
Variance1.6110145
MonotonicityNot monotonic
2023-10-19T20:20:53.726507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 55674
66.5%
1 16105
 
19.2%
2 6167
 
7.4%
3 2774
 
3.3%
4 1341
 
1.6%
5 683
 
0.8%
6 404
 
0.5%
7 233
 
0.3%
8 128
 
0.2%
9 98
 
0.1%
Other values (10) 164
 
0.2%
ValueCountFrequency (%)
0 55674
66.5%
1 16105
 
19.2%
2 6167
 
7.4%
3 2774
 
3.3%
4 1341
 
1.6%
5 683
 
0.8%
6 404
 
0.5%
7 233
 
0.3%
8 128
 
0.2%
9 98
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
15 7
 
< 0.1%
14 8
 
< 0.1%
13 17
 
< 0.1%
12 30
< 0.1%
11 46
0.1%
10 51
0.1%

diag_1
Categorical

HIGH CARDINALITY 

Distinct704
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size839.8 KiB
428
 
5619
414
 
5537
786
 
3219
410
 
2839
486
 
2812
Other values (699)
63745 

Length

Max length6
Median length3
Mean length3.1796087
Min length1

Characters and Unicode

Total characters266359
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)0.1%

Sample

1st row276
2nd row648
3rd row8
4th row197
5th row414

Common Values

ValueCountFrequency (%)
428 5619
 
6.7%
414 5537
 
6.6%
786 3219
 
3.8%
410 2839
 
3.4%
486 2812
 
3.4%
427 2280
 
2.7%
491 1921
 
2.3%
715 1699
 
2.0%
682 1687
 
2.0%
780 1679
 
2.0%
Other values (694) 54479
65.0%

Length

2023-10-19T20:20:53.869501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428 5619
 
6.7%
414 5537
 
6.6%
786 3219
 
3.8%
410 2839
 
3.4%
486 2812
 
3.4%
427 2280
 
2.7%
491 1921
 
2.3%
715 1699
 
2.0%
682 1687
 
2.0%
780 1679
 
2.0%
Other values (694) 54479
65.0%

Most occurring characters

ValueCountFrequency (%)
4 45935
17.2%
2 33049
12.4%
8 31035
11.7%
5 30602
11.5%
7 23226
8.7%
1 22785
8.6%
0 20742
7.8%
6 19171
7.2%
9 16875
 
6.3%
3 14478
 
5.4%
Other values (3) 8461
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 257898
96.8%
Other Punctuation 7156
 
2.7%
Uppercase Letter 1305
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 45935
17.8%
2 33049
12.8%
8 31035
12.0%
5 30602
11.9%
7 23226
9.0%
1 22785
8.8%
0 20742
8.0%
6 19171
7.4%
9 16875
 
6.5%
3 14478
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
V 1304
99.9%
E 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 7156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 265054
99.5%
Latin 1305
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 45935
17.3%
2 33049
12.5%
8 31035
11.7%
5 30602
11.5%
7 23226
8.8%
1 22785
8.6%
0 20742
7.8%
6 19171
7.2%
9 16875
 
6.4%
3 14478
 
5.5%
Latin
ValueCountFrequency (%)
V 1304
99.9%
E 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 266359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 45935
17.2%
2 33049
12.4%
8 31035
11.7%
5 30602
11.5%
7 23226
8.7%
1 22785
8.6%
0 20742
7.8%
6 19171
7.2%
9 16875
 
6.3%
3 14478
 
5.4%
Other values (3) 8461
 
3.2%

diag_2
Categorical

HIGH CARDINALITY 

Distinct726
Distinct (%)0.9%
Missing322
Missing (%)0.4%
Memory size840.1 KiB
276
 
5631
428
 
5371
250
 
4948
427
 
4092
401
 
3124
Other values (721)
60283 

Length

Max length6
Median length3
Mean length3.1699002
Min length1

Characters and Unicode

Total characters264525
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)0.1%

Sample

1st row250.01
2nd row250
3rd row250.43
4th row157
5th row411

Common Values

ValueCountFrequency (%)
276 5631
 
6.7%
428 5371
 
6.4%
250 4948
 
5.9%
427 4092
 
4.9%
401 3124
 
3.7%
599 2726
 
3.3%
496 2691
 
3.2%
403 2380
 
2.8%
414 2169
 
2.6%
411 2122
 
2.5%
Other values (716) 48195
57.5%

Length

2023-10-19T20:20:54.085021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
276 5631
 
6.7%
428 5371
 
6.4%
250 4948
 
5.9%
427 4092
 
4.9%
401 3124
 
3.7%
599 2726
 
3.3%
496 2691
 
3.2%
403 2380
 
2.9%
414 2169
 
2.6%
411 2122
 
2.5%
Other values (716) 48195
57.8%

Most occurring characters

ValueCountFrequency (%)
4 42078
15.9%
2 40946
15.5%
5 31284
11.8%
0 27951
10.6%
7 23669
8.9%
8 23636
8.9%
1 21130
8.0%
9 17991
6.8%
6 16625
 
6.3%
3 11641
 
4.4%
Other values (3) 7574
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 256951
97.1%
Other Punctuation 5405
 
2.0%
Uppercase Letter 2169
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 42078
16.4%
2 40946
15.9%
5 31284
12.2%
0 27951
10.9%
7 23669
9.2%
8 23636
9.2%
1 21130
8.2%
9 17991
7.0%
6 16625
 
6.5%
3 11641
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
V 1571
72.4%
E 598
 
27.6%
Other Punctuation
ValueCountFrequency (%)
. 5405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 262356
99.2%
Latin 2169
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 42078
16.0%
2 40946
15.6%
5 31284
11.9%
0 27951
10.7%
7 23669
9.0%
8 23636
9.0%
1 21130
8.1%
9 17991
6.9%
6 16625
 
6.3%
3 11641
 
4.4%
Latin
ValueCountFrequency (%)
V 1571
72.4%
E 598
 
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 264525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 42078
15.9%
2 40946
15.5%
5 31284
11.8%
0 27951
10.6%
7 23669
8.9%
8 23636
8.9%
1 21130
8.0%
9 17991
6.8%
6 16625
 
6.3%
3 11641
 
4.4%
Other values (3) 7574
 
2.9%

diag_3
Categorical

HIGH CARDINALITY  MISSING 

Distinct764
Distinct (%)0.9%
Missing1164
Missing (%)1.4%
Memory size856.5 KiB
250
9494 
401
6919 
276
 
4278
428
 
3762
427
 
3179
Other values (759)
54975 

Length

Max length6
Median length3
Mean length3.1385113
Min length1

Characters and Unicode

Total characters259263
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)0.2%

Sample

1st row255
2nd rowV27
3rd row403
4th row250
5th row250

Common Values

ValueCountFrequency (%)
250 9494
 
11.3%
401 6919
 
8.3%
276 4278
 
5.1%
428 3762
 
4.5%
427 3179
 
3.8%
414 3041
 
3.6%
496 2089
 
2.5%
403 1985
 
2.4%
585 1738
 
2.1%
272 1633
 
1.9%
Other values (754) 44489
53.1%

Length

2023-10-19T20:20:54.267939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
250 9494
 
11.5%
401 6919
 
8.4%
276 4278
 
5.2%
428 3762
 
4.6%
427 3179
 
3.8%
414 3041
 
3.7%
496 2089
 
2.5%
403 1985
 
2.4%
585 1738
 
2.1%
272 1633
 
2.0%
Other values (754) 44489
53.9%

Most occurring characters

ValueCountFrequency (%)
2 42179
16.3%
4 40447
15.6%
5 34035
13.1%
0 32656
12.6%
7 21779
8.4%
1 20225
7.8%
8 19683
7.6%
9 14157
 
5.5%
6 13577
 
5.2%
3 11713
 
4.5%
Other values (3) 8812
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 250451
96.6%
Other Punctuation 4528
 
1.7%
Uppercase Letter 4284
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 42179
16.8%
4 40447
16.1%
5 34035
13.6%
0 32656
13.0%
7 21779
8.7%
1 20225
8.1%
8 19683
7.9%
9 14157
 
5.7%
6 13577
 
5.4%
3 11713
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
V 3286
76.7%
E 998
 
23.3%
Other Punctuation
ValueCountFrequency (%)
. 4528
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 254979
98.3%
Latin 4284
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 42179
16.5%
4 40447
15.9%
5 34035
13.3%
0 32656
12.8%
7 21779
8.5%
1 20225
7.9%
8 19683
7.7%
9 14157
 
5.6%
6 13577
 
5.3%
3 11713
 
4.6%
Latin
ValueCountFrequency (%)
V 3286
76.7%
E 998
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 259263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 42179
16.3%
4 40447
15.6%
5 34035
13.1%
0 32656
12.6%
7 21779
8.4%
1 20225
7.8%
8 19683
7.6%
9 14157
 
5.5%
6 13577
 
5.2%
3 11713
 
4.5%
Other values (3) 8812
 
3.4%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4821
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-10-19T20:20:54.426253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median9
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9273789
Coefficient of variation (CV)0.25759865
Kurtosis0.033930908
Mean7.4821
Median Absolute Deviation (MAD)0
Skewness-0.92335789
Sum626783
Variance3.7147894
MonotonicityNot monotonic
2023-10-19T20:20:54.554081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 42379
50.6%
5 9355
 
11.2%
8 8463
 
10.1%
7 8308
 
9.9%
6 7500
 
9.0%
4 4457
 
5.3%
3 2206
 
2.6%
2 806
 
1.0%
1 188
 
0.2%
16 42
 
0.1%
Other values (6) 67
 
0.1%
ValueCountFrequency (%)
1 188
 
0.2%
2 806
 
1.0%
3 2206
 
2.6%
4 4457
 
5.3%
5 9355
 
11.2%
6 7500
 
9.0%
7 8308
 
9.9%
8 8463
 
10.1%
9 42379
50.6%
10 16
 
< 0.1%
ValueCountFrequency (%)
16 42
 
0.1%
15 10
 
< 0.1%
14 6
 
< 0.1%
13 16
 
< 0.1%
12 8
 
< 0.1%
11 11
 
< 0.1%
10 16
 
< 0.1%
9 42379
50.6%
8 8463
 
10.1%
7 8308
 
9.9%

max_glu_serum
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)1.0%
Missing83461
Missing (%)99.6%
Memory size736.4 KiB
>300
258 
Norm
45 
>200
 
7

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1240
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>300
2nd rowNorm
3rd rowNorm
4th row>300
5th row>300

Common Values

ValueCountFrequency (%)
>300 258
 
0.3%
Norm 45
 
0.1%
>200 7
 
< 0.1%
(Missing) 83461
99.6%

Length

2023-10-19T20:20:54.728639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:54.887910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
300 258
83.2%
norm 45
 
14.5%
200 7
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 530
42.7%
> 265
21.4%
3 258
20.8%
N 45
 
3.6%
o 45
 
3.6%
r 45
 
3.6%
m 45
 
3.6%
2 7
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 795
64.1%
Math Symbol 265
 
21.4%
Lowercase Letter 135
 
10.9%
Uppercase Letter 45
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 530
66.7%
3 258
32.5%
2 7
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
o 45
33.3%
r 45
33.3%
m 45
33.3%
Math Symbol
ValueCountFrequency (%)
> 265
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1060
85.5%
Latin 180
 
14.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 530
50.0%
> 265
25.0%
3 258
24.3%
2 7
 
0.7%
Latin
ValueCountFrequency (%)
N 45
25.0%
o 45
25.0%
r 45
25.0%
m 45
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 530
42.7%
> 265
21.4%
3 258
20.8%
N 45
 
3.6%
o 45
 
3.6%
r 45
 
3.6%
m 45
 
3.6%
2 7
 
0.6%

A1Cresult
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing69314
Missing (%)82.7%
Memory size736.4 KiB
>8
6795 
Norm
4422 
>7
3240 

Length

Max length4
Median length2
Mean length2.6117452
Min length2

Characters and Unicode

Total characters37758
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>7
2nd row>7
3rd row>8
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
>8 6795
 
8.1%
Norm 4422
 
5.3%
>7 3240
 
3.9%
(Missing) 69314
82.7%

Length

2023-10-19T20:20:55.051594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:55.255696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8 6795
47.0%
norm 4422
30.6%
7 3240
22.4%

Most occurring characters

ValueCountFrequency (%)
> 10035
26.6%
8 6795
18.0%
N 4422
11.7%
o 4422
11.7%
r 4422
11.7%
m 4422
11.7%
7 3240
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13266
35.1%
Math Symbol 10035
26.6%
Decimal Number 10035
26.6%
Uppercase Letter 4422
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4422
33.3%
r 4422
33.3%
m 4422
33.3%
Decimal Number
ValueCountFrequency (%)
8 6795
67.7%
7 3240
32.3%
Math Symbol
ValueCountFrequency (%)
> 10035
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 4422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20070
53.2%
Latin 17688
46.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4422
25.0%
o 4422
25.0%
r 4422
25.0%
m 4422
25.0%
Common
ValueCountFrequency (%)
> 10035
50.0%
8 6795
33.9%
7 3240
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
> 10035
26.6%
8 6795
18.0%
N 4422
11.7%
o 4422
11.7%
r 4422
11.7%
m 4422
11.7%
7 3240
 
8.6%

metformin
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
66862 
Steady
15548 
Up
 
882
Down
 
479

Length

Max length6
Median length2
Mean length2.7538408
Min length2

Characters and Unicode

Total characters230692
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 66862
79.8%
Steady 15548
 
18.6%
Up 882
 
1.1%
Down 479
 
0.6%

Length

2023-10-19T20:20:55.655502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:55.834855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 66862
79.8%
steady 15548
 
18.6%
up 882
 
1.1%
down 479
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 67341
29.2%
N 66862
29.0%
S 15548
 
6.7%
t 15548
 
6.7%
e 15548
 
6.7%
a 15548
 
6.7%
d 15548
 
6.7%
y 15548
 
6.7%
U 882
 
0.4%
p 882
 
0.4%
Other values (3) 1437
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 146921
63.7%
Uppercase Letter 83771
36.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 67341
45.8%
t 15548
 
10.6%
e 15548
 
10.6%
a 15548
 
10.6%
d 15548
 
10.6%
y 15548
 
10.6%
p 882
 
0.6%
w 479
 
0.3%
n 479
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 66862
79.8%
S 15548
 
18.6%
U 882
 
1.1%
D 479
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 230692
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 67341
29.2%
N 66862
29.0%
S 15548
 
6.7%
t 15548
 
6.7%
e 15548
 
6.7%
a 15548
 
6.7%
d 15548
 
6.7%
y 15548
 
6.7%
U 882
 
0.4%
p 882
 
0.4%
Other values (3) 1437
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 67341
29.2%
N 66862
29.0%
S 15548
 
6.7%
t 15548
 
6.7%
e 15548
 
6.7%
a 15548
 
6.7%
d 15548
 
6.7%
y 15548
 
6.7%
U 882
 
0.4%
p 882
 
0.4%
Other values (3) 1437
 
0.6%

insulin
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
37861 
Steady
26175 
Down
10408 
Up
9327 

Length

Max length6
Median length2
Mean length3.4983228
Min length2

Characters and Unicode

Total characters293058
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUp
2nd rowNo
3rd rowUp
4th rowSteady
5th rowSteady

Common Values

ValueCountFrequency (%)
No 37861
45.2%
Steady 26175
31.2%
Down 10408
 
12.4%
Up 9327
 
11.1%

Length

2023-10-19T20:20:56.021250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:56.217997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 37861
45.2%
steady 26175
31.2%
down 10408
 
12.4%
up 9327
 
11.1%

Most occurring characters

ValueCountFrequency (%)
o 48269
16.5%
N 37861
12.9%
S 26175
8.9%
t 26175
8.9%
e 26175
8.9%
a 26175
8.9%
d 26175
8.9%
y 26175
8.9%
D 10408
 
3.6%
w 10408
 
3.6%
Other values (3) 29062
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 209287
71.4%
Uppercase Letter 83771
28.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 48269
23.1%
t 26175
12.5%
e 26175
12.5%
a 26175
12.5%
d 26175
12.5%
y 26175
12.5%
w 10408
 
5.0%
n 10408
 
5.0%
p 9327
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N 37861
45.2%
S 26175
31.2%
D 10408
 
12.4%
U 9327
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 293058
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 48269
16.5%
N 37861
12.9%
S 26175
8.9%
t 26175
8.9%
e 26175
8.9%
a 26175
8.9%
d 26175
8.9%
y 26175
8.9%
D 10408
 
3.6%
w 10408
 
3.6%
Other values (3) 29062
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 48269
16.5%
N 37861
12.9%
S 26175
8.9%
t 26175
8.9%
e 26175
8.9%
a 26175
8.9%
d 26175
8.9%
y 26175
8.9%
D 10408
 
3.6%
w 10408
 
3.6%
Other values (3) 29062
9.9%

change
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size736.4 KiB
No
44209 
Ch
39562 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters167542
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCh
2nd rowNo
3rd rowCh
4th rowCh
5th rowNo

Common Values

ValueCountFrequency (%)
No 44209
52.8%
Ch 39562
47.2%

Length

2023-10-19T20:20:56.402920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:56.594570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 44209
52.8%
ch 39562
47.2%

Most occurring characters

ValueCountFrequency (%)
N 44209
26.4%
o 44209
26.4%
C 39562
23.6%
h 39562
23.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 83771
50.0%
Lowercase Letter 83771
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 44209
52.8%
C 39562
47.2%
Lowercase Letter
ValueCountFrequency (%)
o 44209
52.8%
h 39562
47.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 167542
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 44209
26.4%
o 44209
26.4%
C 39562
23.6%
h 39562
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 44209
26.4%
o 44209
26.4%
C 39562
23.6%
h 39562
23.6%

diabetesMed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size736.4 KiB
Yes
65278 
No
18493 

Length

Max length3
Median length3
Mean length2.7792434
Min length2

Characters and Unicode

Total characters232820
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 65278
77.9%
No 18493
 
22.1%

Length

2023-10-19T20:20:56.773850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:56.913266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yes 65278
77.9%
no 18493
 
22.1%

Most occurring characters

ValueCountFrequency (%)
Y 65278
28.0%
e 65278
28.0%
s 65278
28.0%
N 18493
 
7.9%
o 18493
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149049
64.0%
Uppercase Letter 83771
36.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 65278
43.8%
s 65278
43.8%
o 18493
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
Y 65278
77.9%
N 18493
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 232820
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 65278
28.0%
e 65278
28.0%
s 65278
28.0%
N 18493
 
7.9%
o 18493
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 65278
28.0%
e 65278
28.0%
s 65278
28.0%
N 18493
 
7.9%
o 18493
 
7.9%

readmitted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
NO
74240 
<30
9531 

Length

Max length3
Median length2
Mean length2.1137745
Min length2

Characters and Unicode

Total characters177073
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 74240
88.6%
<30 9531
 
11.4%

Length

2023-10-19T20:20:57.085021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:57.262629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 74240
88.6%
30 9531
 
11.4%

Most occurring characters

ValueCountFrequency (%)
N 74240
41.9%
O 74240
41.9%
< 9531
 
5.4%
3 9531
 
5.4%
0 9531
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 148480
83.9%
Decimal Number 19062
 
10.8%
Math Symbol 9531
 
5.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 74240
50.0%
O 74240
50.0%
Decimal Number
ValueCountFrequency (%)
3 9531
50.0%
0 9531
50.0%
Math Symbol
ValueCountFrequency (%)
< 9531
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 148480
83.9%
Common 28593
 
16.1%

Most frequent character per script

Common
ValueCountFrequency (%)
< 9531
33.3%
3 9531
33.3%
0 9531
33.3%
Latin
ValueCountFrequency (%)
N 74240
50.0%
O 74240
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 74240
41.9%
O 74240
41.9%
< 9531
 
5.4%
3 9531
 
5.4%
0 9531
 
5.4%

Meglitinides
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
81692 
Steady
 
1905
Up
 
124
Down
 
50

Length

Max length6
Median length2
Mean length2.092156
Min length2

Characters and Unicode

Total characters175262
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 81692
97.5%
Steady 1905
 
2.3%
Up 124
 
0.1%
Down 50
 
0.1%

Length

2023-10-19T20:20:57.422895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:57.601411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 81692
97.5%
steady 1905
 
2.3%
up 124
 
0.1%
down 50
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 81742
46.6%
N 81692
46.6%
S 1905
 
1.1%
t 1905
 
1.1%
e 1905
 
1.1%
a 1905
 
1.1%
d 1905
 
1.1%
y 1905
 
1.1%
U 124
 
0.1%
p 124
 
0.1%
Other values (3) 150
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91491
52.2%
Uppercase Letter 83771
47.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 81742
89.3%
t 1905
 
2.1%
e 1905
 
2.1%
a 1905
 
2.1%
d 1905
 
2.1%
y 1905
 
2.1%
p 124
 
0.1%
w 50
 
0.1%
n 50
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 81692
97.5%
S 1905
 
2.3%
U 124
 
0.1%
D 50
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 175262
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 81742
46.6%
N 81692
46.6%
S 1905
 
1.1%
t 1905
 
1.1%
e 1905
 
1.1%
a 1905
 
1.1%
d 1905
 
1.1%
y 1905
 
1.1%
U 124
 
0.1%
p 124
 
0.1%
Other values (3) 150
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 81742
46.6%
N 81692
46.6%
S 1905
 
1.1%
t 1905
 
1.1%
e 1905
 
1.1%
a 1905
 
1.1%
d 1905
 
1.1%
y 1905
 
1.1%
U 124
 
0.1%
p 124
 
0.1%
Other values (3) 150
 
0.1%

Thiazolidinediones
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
72268 
Steady
11019 
Up
 
330
Down
 
154

Length

Max length6
Median length2
Mean length2.5298254
Min length2

Characters and Unicode

Total characters211926
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 72268
86.3%
Steady 11019
 
13.2%
Up 330
 
0.4%
Down 154
 
0.2%

Length

2023-10-19T20:20:57.742020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:57.901712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 72268
86.3%
steady 11019
 
13.2%
up 330
 
0.4%
down 154
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 72422
34.2%
N 72268
34.1%
S 11019
 
5.2%
t 11019
 
5.2%
e 11019
 
5.2%
a 11019
 
5.2%
d 11019
 
5.2%
y 11019
 
5.2%
U 330
 
0.2%
p 330
 
0.2%
Other values (3) 462
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128155
60.5%
Uppercase Letter 83771
39.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 72422
56.5%
t 11019
 
8.6%
e 11019
 
8.6%
a 11019
 
8.6%
d 11019
 
8.6%
y 11019
 
8.6%
p 330
 
0.3%
w 154
 
0.1%
n 154
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 72268
86.3%
S 11019
 
13.2%
U 330
 
0.4%
D 154
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 211926
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 72422
34.2%
N 72268
34.1%
S 11019
 
5.2%
t 11019
 
5.2%
e 11019
 
5.2%
a 11019
 
5.2%
d 11019
 
5.2%
y 11019
 
5.2%
U 330
 
0.2%
p 330
 
0.2%
Other values (3) 462
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 72422
34.2%
N 72268
34.1%
S 11019
 
5.2%
t 11019
 
5.2%
e 11019
 
5.2%
a 11019
 
5.2%
d 11019
 
5.2%
y 11019
 
5.2%
U 330
 
0.2%
p 330
 
0.2%
Other values (3) 462
 
0.2%

Sulfonylureas
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
60330 
Steady
20855 
Up
 
1529
Down
 
1057

Length

Max length6
Median length2
Mean length3.0210455
Min length2

Characters and Unicode

Total characters253076
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSteady
3rd rowNo
4th rowSteady
5th rowNo

Common Values

ValueCountFrequency (%)
No 60330
72.0%
Steady 20855
 
24.9%
Up 1529
 
1.8%
Down 1057
 
1.3%

Length

2023-10-19T20:20:58.084646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:58.284816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 60330
72.0%
steady 20855
 
24.9%
up 1529
 
1.8%
down 1057
 
1.3%

Most occurring characters

ValueCountFrequency (%)
o 61387
24.3%
N 60330
23.8%
S 20855
 
8.2%
t 20855
 
8.2%
e 20855
 
8.2%
a 20855
 
8.2%
d 20855
 
8.2%
y 20855
 
8.2%
U 1529
 
0.6%
p 1529
 
0.6%
Other values (3) 3171
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 169305
66.9%
Uppercase Letter 83771
33.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 61387
36.3%
t 20855
 
12.3%
e 20855
 
12.3%
a 20855
 
12.3%
d 20855
 
12.3%
y 20855
 
12.3%
p 1529
 
0.9%
w 1057
 
0.6%
n 1057
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 60330
72.0%
S 20855
 
24.9%
U 1529
 
1.8%
D 1057
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 253076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 61387
24.3%
N 60330
23.8%
S 20855
 
8.2%
t 20855
 
8.2%
e 20855
 
8.2%
a 20855
 
8.2%
d 20855
 
8.2%
y 20855
 
8.2%
U 1529
 
0.6%
p 1529
 
0.6%
Other values (3) 3171
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 61387
24.3%
N 60330
23.8%
S 20855
 
8.2%
t 20855
 
8.2%
e 20855
 
8.2%
a 20855
 
8.2%
d 20855
 
8.2%
y 20855
 
8.2%
U 1529
 
0.6%
p 1529
 
0.6%
Other values (3) 3171
 
1.3%

Alpha-Gluc_Inhibitors
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
83485 
Steady
 
269
Up
 
9
Down
 
8

Length

Max length6
Median length2
Mean length2.0130355
Min length2

Characters and Unicode

Total characters168634
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 83485
99.7%
Steady 269
 
0.3%
Up 9
 
< 0.1%
Down 8
 
< 0.1%

Length

2023-10-19T20:20:58.477387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:58.634903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 83485
99.7%
steady 269
 
0.3%
up 9
 
< 0.1%
down 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 83493
49.5%
N 83485
49.5%
S 269
 
0.2%
t 269
 
0.2%
e 269
 
0.2%
a 269
 
0.2%
d 269
 
0.2%
y 269
 
0.2%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84863
50.3%
Uppercase Letter 83771
49.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 83493
98.4%
t 269
 
0.3%
e 269
 
0.3%
a 269
 
0.3%
d 269
 
0.3%
y 269
 
0.3%
p 9
 
< 0.1%
w 8
 
< 0.1%
n 8
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 83485
99.7%
S 269
 
0.3%
U 9
 
< 0.1%
D 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 168634
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 83493
49.5%
N 83485
49.5%
S 269
 
0.2%
t 269
 
0.2%
e 269
 
0.2%
a 269
 
0.2%
d 269
 
0.2%
y 269
 
0.2%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 83493
49.5%
N 83485
49.5%
S 269
 
0.2%
t 269
 
0.2%
e 269
 
0.2%
a 269
 
0.2%
d 269
 
0.2%
y 269
 
0.2%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 24
 
< 0.1%

Combination Therapies
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
83070 
Steady
 
688
Up
 
7
Down
 
6

Length

Max length6
Median length2
Mean length2.0329947
Min length2

Characters and Unicode

Total characters170306
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 83070
99.2%
Steady 688
 
0.8%
Up 7
 
< 0.1%
Down 6
 
< 0.1%

Length

2023-10-19T20:20:58.808931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:59.018029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 83070
99.2%
steady 688
 
0.8%
up 7
 
< 0.1%
down 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 83076
48.8%
N 83070
48.8%
S 688
 
0.4%
t 688
 
0.4%
e 688
 
0.4%
a 688
 
0.4%
d 688
 
0.4%
y 688
 
0.4%
U 7
 
< 0.1%
p 7
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 86535
50.8%
Uppercase Letter 83771
49.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 83076
96.0%
t 688
 
0.8%
e 688
 
0.8%
a 688
 
0.8%
d 688
 
0.8%
y 688
 
0.8%
p 7
 
< 0.1%
w 6
 
< 0.1%
n 6
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 83070
99.2%
S 688
 
0.8%
U 7
 
< 0.1%
D 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 170306
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 83076
48.8%
N 83070
48.8%
S 688
 
0.4%
t 688
 
0.4%
e 688
 
0.4%
a 688
 
0.4%
d 688
 
0.4%
y 688
 
0.4%
U 7
 
< 0.1%
p 7
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 83076
48.8%
N 83070
48.8%
S 688
 
0.4%
t 688
 
0.4%
e 688
 
0.4%
a 688
 
0.4%
d 688
 
0.4%
y 688
 
0.4%
U 7
 
< 0.1%
p 7
 
< 0.1%
Other values (3) 18
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Home
54547 
Institution
17980 
Home_With_Support
11244 

Length

Max length17
Median length4
Mean length7.247329
Min length4

Characters and Unicode

Total characters607116
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowHome
3rd rowHome
4th rowHome
5th rowHome

Common Values

ValueCountFrequency (%)
Home 54547
65.1%
Institution 17980
 
21.5%
Home_With_Support 11244
 
13.4%

Length

2023-10-19T20:20:59.192639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:59.319664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
home 54547
65.1%
institution 17980
 
21.5%
home_with_support 11244
 
13.4%

Most occurring characters

ValueCountFrequency (%)
o 95015
15.7%
t 76428
12.6%
H 65791
10.8%
m 65791
10.8%
e 65791
10.8%
i 47204
7.8%
n 35960
 
5.9%
u 29224
 
4.8%
_ 22488
 
3.7%
p 22488
 
3.7%
Other values (6) 80936
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 478369
78.8%
Uppercase Letter 106259
 
17.5%
Connector Punctuation 22488
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 95015
19.9%
t 76428
16.0%
m 65791
13.8%
e 65791
13.8%
i 47204
9.9%
n 35960
 
7.5%
u 29224
 
6.1%
p 22488
 
4.7%
s 17980
 
3.8%
h 11244
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
H 65791
61.9%
I 17980
 
16.9%
W 11244
 
10.6%
S 11244
 
10.6%
Connector Punctuation
ValueCountFrequency (%)
_ 22488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 584628
96.3%
Common 22488
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 95015
16.3%
t 76428
13.1%
H 65791
11.3%
m 65791
11.3%
e 65791
11.3%
i 47204
8.1%
n 35960
 
6.2%
u 29224
 
5.0%
p 22488
 
3.8%
I 17980
 
3.1%
Other values (5) 62956
10.8%
Common
ValueCountFrequency (%)
_ 22488
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 607116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 95015
15.7%
t 76428
12.6%
H 65791
10.8%
m 65791
10.8%
e 65791
10.8%
i 47204
7.8%
n 35960
 
5.9%
u 29224
 
4.8%
_ 22488
 
3.7%
p 22488
 
3.7%
Other values (6) 80936
13.3%

Admission_Source
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Emerg_Dept
51543 
Referral
26746 
Transfer
5482 

Length

Max length10
Median length10
Mean length9.2305691
Min length8

Characters and Unicode

Total characters773254
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmerg_Dept
2nd rowEmerg_Dept
3rd rowEmerg_Dept
4th rowEmerg_Dept
5th rowReferral

Common Values

ValueCountFrequency (%)
Emerg_Dept 51543
61.5%
Referral 26746
31.9%
Transfer 5482
 
6.5%

Length

2023-10-19T20:20:59.501531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T20:20:59.686871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
emerg_dept 51543
61.5%
referral 26746
31.9%
transfer 5482
 
6.5%

Most occurring characters

ValueCountFrequency (%)
e 162060
21.0%
r 115999
15.0%
E 51543
 
6.7%
m 51543
 
6.7%
g 51543
 
6.7%
_ 51543
 
6.7%
D 51543
 
6.7%
p 51543
 
6.7%
t 51543
 
6.7%
f 32228
 
4.2%
Other values (6) 102166
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 586397
75.8%
Uppercase Letter 135314
 
17.5%
Connector Punctuation 51543
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 162060
27.6%
r 115999
19.8%
m 51543
 
8.8%
g 51543
 
8.8%
p 51543
 
8.8%
t 51543
 
8.8%
f 32228
 
5.5%
a 32228
 
5.5%
l 26746
 
4.6%
n 5482
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
E 51543
38.1%
D 51543
38.1%
R 26746
19.8%
T 5482
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_ 51543
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 721711
93.3%
Common 51543
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 162060
22.5%
r 115999
16.1%
E 51543
 
7.1%
m 51543
 
7.1%
g 51543
 
7.1%
D 51543
 
7.1%
p 51543
 
7.1%
t 51543
 
7.1%
f 32228
 
4.5%
a 32228
 
4.5%
Other values (5) 69938
9.7%
Common
ValueCountFrequency (%)
_ 51543
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 773254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 162060
21.0%
r 115999
15.0%
E 51543
 
6.7%
m 51543
 
6.7%
g 51543
 
6.7%
_ 51543
 
6.7%
D 51543
 
6.7%
p 51543
 
6.7%
t 51543
 
6.7%
f 32228
 
4.2%
Other values (6) 102166
13.2%

Interactions

2023-10-19T20:20:46.858972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:37.799175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:39.079867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:40.327011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.624724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:42.933507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:44.135576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:45.513837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:47.054433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:37.962840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:39.225214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:40.525300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.768013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.060328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:44.291549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:45.687041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:47.226538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:38.133420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:39.378514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:40.696073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.932350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.212255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:44.449171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:45.863797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:47.380554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:38.278492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:39.545376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:40.846301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:42.125540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.355638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:44.636389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:46.009235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:47.555643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:38.443830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:39.689955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.005821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:42.289077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.546417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:44.818269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:46.203556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:47.713586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:38.563761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:39.842380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.151270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:42.433413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.686745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:44.974667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:46.365824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:47.882108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:38.756396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:40.016625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.325408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:42.610642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.830211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:45.135024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:46.517184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:48.064426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:38.905450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:40.176637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:41.457954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:42.766318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:43.961132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:45.309035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-19T20:20:46.679137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-19T20:20:59.834718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
time_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesracegenderageadmission_type_idmax_glu_serumA1CresultmetformininsulinchangediabetesMedreadmittedMeglitinidesThiazolidinedionesSulfonylureasAlpha-Gluc_InhibitorsCombination TherapiesDischarge_LocationAdmission_Source
time_in_hospital1.0000.3550.1800.4660.0010.0060.0910.2520.0160.0400.0430.0300.1170.0290.0280.0810.1190.0720.0530.0250.0290.0560.0080.0040.2240.057
num_lab_procedures0.3551.000-0.0060.2670.0210.0340.0440.1740.0420.0260.0250.1630.0770.0260.0360.0840.0880.0540.0290.0160.0230.0380.0000.0020.0760.181
num_procedures0.180-0.0061.0000.338-0.018-0.042-0.0650.0620.0290.0690.0650.1640.0000.0200.0260.0220.0250.0320.0180.0000.0110.0150.0000.0000.0660.185
num_medications0.4660.2670.3381.0000.0780.0490.1070.3000.0350.0520.0620.0930.1410.0320.0440.1430.2450.1930.0480.0230.0530.0600.0130.0050.1460.083
number_outpatient0.0010.021-0.0180.0781.0000.1800.1690.1180.0130.0050.0020.0070.0480.0140.0050.0150.0020.0000.0000.0000.0000.0000.0000.0000.0290.013
number_emergency0.0060.034-0.0420.0490.1801.0000.2270.0890.0060.0120.0280.0100.0000.0120.0000.0190.0170.0060.0300.0060.0000.0000.0000.0200.0150.017
number_inpatient0.0910.044-0.0650.1070.1690.2271.0000.1400.0060.0130.0440.0200.0000.0260.0300.0460.0200.0200.1430.0000.0150.0250.0120.0000.0450.042
number_diagnoses0.2520.1740.0620.3000.1180.0890.1401.0000.0560.0020.1360.0790.1110.1170.0490.0750.0480.0250.0530.0290.0160.0260.0000.0150.1510.192
race0.0160.0420.0290.0350.0130.0060.0060.0561.0000.0750.0970.0580.0000.0560.0130.0430.0230.0170.0080.0140.0150.0280.0070.0100.0530.043
gender0.0400.0260.0690.0520.0050.0120.0130.0020.0751.0000.1130.0180.0000.0310.0000.0050.0150.0160.0000.0050.0130.0420.0150.0000.0780.021
age0.0430.0250.0650.0620.0020.0280.0440.1360.0970.1131.0000.0520.1050.1860.0660.0670.0560.0400.0380.0330.0420.0750.0080.0100.2420.074
admission_type_id0.0300.1630.1640.0930.0070.0100.0200.0790.0580.0180.0521.0000.1150.0370.0310.0360.0310.0140.0170.0300.0260.0180.0050.0190.0420.554
max_glu_serum0.1170.0770.0000.1410.0480.0000.0000.1110.0000.0000.1050.1151.0000.3610.0000.0000.0000.0620.0000.0000.0000.1120.0000.0000.0570.076
A1Cresult0.0290.0260.0200.0320.0140.0120.0260.1170.0560.0310.1860.0370.3611.0000.0530.1530.1880.1880.0000.0310.0140.0650.0050.0000.0730.011
metformin0.0280.0360.0260.0440.0050.0000.0300.0490.0130.0000.0660.0310.0000.0531.0000.0300.3290.2680.0250.0110.0680.1070.0110.0140.0320.034
insulin0.0810.0840.0220.1430.0150.0190.0460.0750.0430.0050.0670.0360.0000.1530.0301.0000.6400.5860.0460.0160.0160.0580.0130.0050.0450.071
change0.1190.0880.0250.2450.0020.0170.0200.0480.0230.0150.0560.0310.0000.1880.3290.6401.0000.5030.0220.1000.2930.3440.0480.0450.0410.043
diabetesMed0.0720.0540.0320.1930.0000.0060.0200.0250.0170.0160.0400.0140.0620.1880.2680.5860.5031.0000.0260.0850.2120.3320.0310.0490.0130.008
readmitted0.0530.0290.0180.0480.0000.0300.1430.0530.0080.0000.0380.0170.0000.0000.0250.0460.0220.0261.0000.0070.0080.0070.0070.0000.0950.020
Meglitinides0.0250.0160.0000.0230.0000.0060.0000.0290.0140.0050.0330.0300.0000.0310.0110.0160.1000.0850.0071.0000.0240.0240.0130.0040.0220.017
Thiazolidinediones0.0290.0230.0110.0530.0000.0000.0150.0160.0150.0130.0420.0260.0000.0140.0680.0160.2930.2120.0080.0241.0000.0650.0080.0160.0100.030
Sulfonylureas0.0560.0380.0150.0600.0000.0000.0250.0260.0280.0420.0750.0180.1120.0650.1070.0580.3440.3320.0070.0240.0651.0000.0260.0220.0280.020
Alpha-Gluc_Inhibitors0.0080.0000.0000.0130.0000.0000.0120.0000.0070.0150.0080.0050.0000.0050.0110.0130.0480.0310.0070.0130.0080.0261.0000.0040.0060.005
Combination Therapies0.0040.0020.0000.0050.0000.0200.0000.0150.0100.0000.0100.0190.0000.0000.0140.0050.0450.0490.0000.0040.0160.0220.0041.0000.0040.021
Discharge_Location0.2240.0760.0660.1460.0290.0150.0450.1510.0530.0780.2420.0420.0570.0730.0320.0450.0410.0130.0950.0220.0100.0280.0060.0041.0000.061
Admission_Source0.0570.1810.1850.0830.0130.0170.0420.1920.0430.0210.0740.5540.0760.0110.0340.0710.0430.0080.0200.0170.0300.0200.0050.0210.0611.000

Missing values

2023-10-19T20:20:48.392026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-19T20:20:49.078554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-19T20:20:49.695160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

racegenderageadmission_type_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1CresultmetformininsulinchangediabetesMedreadmittedMeglitinidesThiazolidinedionesSulfonylureasAlpha-Gluc_InhibitorsCombination TherapiesDischarge_LocationAdmission_Source
1CaucasianFemale[10-20)1359018000276250.012559NaNNaNNoUpChYesNONoNoNoNoNoHomeEmerg_Dept
2AfricanAmericanFemale[20-30)1211513201648250V276NaNNaNNoNoNoYesNONoNoSteadyNoNoHomeEmerg_Dept
3CaucasianMale[30-40)12441160008250.434037NaNNaNNoUpChYesNONoNoNoNoNoHomeEmerg_Dept
4CaucasianMale[40-50)1151080001971572505NaNNaNNoSteadyChYesNONoNoSteadyNoNoHomeEmerg_Dept
5CaucasianMale[50-60)23316160004144112509NaNNaNNoSteadyNoYesNONoNoNoNoNoHomeReferral
6CaucasianMale[60-70)3470121000414411V457NaNNaNSteadySteadyChYesNONoNoSteadyNoNoHomeReferral
7CaucasianMale[70-80)15730120004284922508NaNNaNNoNoNoYesNONoNoSteadyNoNoHomeEmerg_Dept
8CaucasianFemale[80-90)21368228000398427388NaNNaNNoSteadyChYesNONoNoSteadyNoNoHomeTransfer
9CaucasianFemale[90-100)312333180004341984868NaNNaNNoSteadyChYesNONoSteadyNoNoNoInstitutionTransfer
10AfricanAmericanFemale[40-50)1947217000250.74039969NaNNaNNoSteadyNoYesNONoNoNoNoNoHomeEmerg_Dept
racegenderageadmission_type_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1CresultmetformininsulinchangediabetesMedreadmittedMeglitinidesThiazolidinedionesSulfonylureasAlpha-Gluc_InhibitorsCombination TherapiesDischarge_LocationAdmission_Source
101756OtherFemale[60-70)12466171119965854039NaNNaNNoSteadyNoYesNONoNoNoNoNoHomeEmerg_Dept
101757CaucasianFemale[70-80)15211160014915185119NaNNaNNoSteadyNoYesNONoNoNoNoNoHomeEmerg_Dept
101758CaucasianFemale[80-90)157612201029283049NaNNaNNoUpChYesNONoNoNoNoNoHomeEmerg_Dept
101759CaucasianMale[80-90)1110153004357842507NaNNaNNoUpChYesNONoNoNoNoNoHomeEmerg_Dept
101760AfricanAmericanFemale[60-70)16451253123454384129NaNNaNNoDownChYesNONoSteadyNoNoNoHomeEmerg_Dept
101761AfricanAmericanMale[70-80)1351016000250.132914589NaN>8SteadyDownChYesNONoNoNoNoNoInstitutionEmerg_Dept
101762AfricanAmericanFemale[80-90)15333180015602767879NaNNaNNoSteadyNoYesNONoNoNoNoNoInstitutionTransfer
101763CaucasianMale[70-80)1153091003859029613NaNNaNSteadyDownChYesNONoNoNoNoNoHomeEmerg_Dept
101764CaucasianFemale[80-90)210452210019962859989NaNNaNNoUpChYesNONoSteadySteadyNoNoInstitutionEmerg_Dept
101765CaucasianMale[70-80)1613330005305307879NaNNaNNoNoNoNoNONoNoNoNoNoHomeEmerg_Dept